Autonomous Vehicle Safety: An Interdisciplinary Challenge

Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory approaches. Significant open technical challenges include validating inductive learning in the face of novel environmental inputs and achieving the very high levels of dependability required for full-scale fleet deployment. However, the biggest challenge may be in creating an end-to-end design and deployment process that integrates the safety concerns of a myriad of technical specialties into a unified approach.

• Record URL:
  https://doi.org/10.1109/MITS.2016.2583491
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • Koopman, Philip
  • Wagner, Michael
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 90-96
• Serial:
  • IEEE Intelligent Transportation Systems Magazine
  • Volume: 9
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1939-1390
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645

Subject/Index Terms

• TRT Terms: Deployment; Highway safety; Intelligent vehicles; Machine learning; Vehicle design; Vehicle safety
• Uncontrolled Terms: Interdisciplinary approach
• Subject Areas: Data and Information Technology; Design; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01630296
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 27 2017 9:34AM